Spatial Variation in Microbial Community in Response to As and Pb Contamination in Paddy Soils Near a Pb-Zn Mining Site

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Abstract

Mining activity is a growing environmental concern as it contributes to heavy metals (HMs) pollution in agricultural soils. Microbial communities play an important role in the biogeochemical cycling of HMs and have the potential to be used as bioindicators. Arsenic (As) and lead (Pb) are the most hazardous HMs and are mainly originated from mining activities. However, spatial variation in microbial community in response to As and Pb contamination in paddy soils remains overlooked. In this study, the biological and chemical properties of sixteen soil samples from four sites (N01, N02, N03, and N04) near a Pb-Zn mining site at different As and Pb levels were examined. The results showed that soil pH, total As and Pb, bioavailable As and Pb, nitrate-nitrogen (NO ₃ ⁻-N) and ammonia-nitrogen (NH ₄ ⁺-N) were the most important factors in shaping the bacterial community structure. In addition, significant correlations between various bacterial genera and As and Pb concentrations were observed, indicating their potential roles in As and Pb biogeochemical cycling. These findings provide insights into the variation of paddy soil bacterial community in soils co-contaminated with different levels of As and Pb.

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QIIME allows analysis of high-throughput community sequencing data.

J. Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh … (2010)

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Record: found
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Article: not found

Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

Qiong Wang, George Garrity, James Tiedje … (2007)

The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.